Dataset opportunity
Swtchenergy — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Swtchenergy, usable for Predictive Maintenance and Anomaly Detection.
Score
45
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
63%
Action
Data Sharing Agreement
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
Global Predictive Maintenance Market was valued at $10.93 billion in 2024, projected to grow at a CAGR of 26.5% (2025-2032).
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Partial
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Swtchenergy holds a valuable Mobility Telemetry Dataset composed of Time Series data from its electric vehicle charging network. The dataset includes `iot_data`, `geo_data`, and `event_streams` that capture real-world operational metrics, making it highly suitable for developing and training Predictive Maintenance models to anticipate equipment failures in charging stations.
The business value is significant, as this data addresses the global Predictive Maintenance market, which was valued at $10.93 billion in 2024 and is projected to grow at a CAGR of 26.5%. [6] Despite access complexities such as shared data ownership, the presence of sensitive PII, and the need for proprietary API integration, the rarity and richness of this real-world operational data make it a critical asset for AI buyers aiming to innovate in this high-growth sector. [6] ⚠ Diligence (valuable data, access to negotiate): Data ownership is likely shared with property owners (MURBs) and individual drivers; Contains sensitive PII including charging history, location, and payment data; Access requires technical integration via their proprietary SWTCH Connect API · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Public evidence confirms Swtchenergy captures a proprietary, high-rarity stream of EV charger telemetry, including real-time performance patterns and error logs. This dataset is a critical asset for Industrial AI vendors developing predictive maintenance models, enabling them to diagnose and prevent hardware failures. In a global market projected to exceed $10 billion and growing at over 26% annually, this data provides a direct path to capturing value by optimizing the uptime of critical charging infrastructure.
See dimension details ↓- Dataset Specificity100
dominant 'iot_data', sector mobility, 4 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity70
proprietary domain data (open lowers rarity)
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume64
5 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
real-time/streaming
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value94
fit for Predictive Maintenance
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
AI buyer demand is high, driven by the need for real-world operational data to build models for the Predictive Maintenance market, which is growing at a 26.5% CAGR. [6]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility48
open/API access
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility66
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength86
5 evidence types, 5 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License28
ownership=mixed, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0 data-appetite signals (0 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 5 recent external signals — proprietary data beyond what's already monetised
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit50
⚠ review — Swtchenergy's core business is selling intelligent EV charging software and energy management solutions, making it a technology vendor already on the market, not a holder of dormant data. Issues: Company's core product is selling intelligence/software (SWTCH Cortex, SWTCH Control) which is an exclusion criterion.; The company's business model is to provide a platform for building owners to manage and monetize EV charging, which is a form of data-as-a-service/intelligence-; The company actively markets its ability to generate revenue for clients through its platform, indicating it is not a holder of 'dormant' data.
- Deep Qualification90
✓ pass — The opportunity is coherent with the target's business model, but access to the data is complex due to shared ownership with property owners and drivers, and strict privacy regulations (PIPEDA) governing personal and vehicle information.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “Portée par un intérêt bien plus fort qu'anticipé de la part des distributeurs, Zeekr accélère son déploiement en France. La marque premium de Geely vise 70 points de vente dès 2027, avec l'ambition de s'imposer rapidement parmi les nouveaux acteurs sur ce segment.”
- “Depuis le premier Kangoo ZE sorti de l'usine de Maubeuge (59) en 2010, la place des véhicules électriques a bien changé dans les sites de production du groupe Renault. Aujourd'hui, le constructeur vient de franchir le cap du million de VE fabriqués en France.”
- “La filiale du groupe Société générale a levé le voile sur Access Lease, sa dernière offre en date. En proposant une formule de location longue durée aux distributeurs automobiles, CGI Finance compte à la fois accentuer la fidélité des clients en points de vente et sécuriser davantage l'approvisionnement des parcs de voitures d'occasion.”
Downloads / exports
This evidence indicates the company captures user interaction data from its mobile and web applications, providing valuable context on charging session initiation for behavioral analysis.
IoT / sensor data
The platform generates time-series IoT data by tracking energy charge and discharge cycles, creating a detailed history of energy consumption for each session.
Industrial data
The company collects industrial sensor data that monitors building-level electrical loads and capacity, providing critical context on the operational environment of the charging hardware.
Event streams
Swtchenergy generates real-time event streams containing comprehensive diagnostic information, including performance patterns and error logs, which are essential for training failure prediction algorithms.
Geospatial data
The dataset includes geospatial data on a network of over 15,000 chargers, enabling analysis segmented by property type and location to refine maintenance models.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Swtchenergy Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market was valued at $10.93 billion in 2024, projected to grow at a CAGR of 26.5% (2025-2032). [6]. Investment score 45.0/100 (confidence 0.63). Recommended action: Data Sharing Agreement.
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